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The feasibility of coexisting a quantum channel with carrier-grade classical optical channels over Hollow Core Nested Antiresonant Nodeless Fibre (HC-NANF) is experimentally explored for the first time in terms of achievable quantum bit error rate (QBER), secret key rate (SKR) as well as classical signal bit error rates (BER). A coexistence transmission of 1.6 Tbps is achieved for the classical channels simultaneously with a quantum channel over a 2 km-long HC-NANF with a total coexistence power of 0 dBm. To find the best and worst wavelength position for the classical channels, we simulated different classical channels bands with different spacing between the quantum and classical channels considering the crosstalk generated from both Raman scattering and four-wave-mixing (FWM) on the quantum channel. Following our simulation, we numerically estimate the best (Raman spectrum dip) and worst locations (Raman spectrum peak) of the classical channel with respect to its impact on the performance on the quantum channel in terms of SKR and QBER. We further implemented a testbed to experimentally test both single-mode fibre (SMF) and HC-NANF in the best and worst-case scenarios. In the best-case scenario, the spacing between quantum and classical is 200 GHz (1.6 nm) with 50 GHz (0.4 nm) spacing between each classical channel. The SKR was preserved without any noticeable changes when coexisting the quantum channel with eight classical channels at 0 dBm total coexistence power in HC-NANF compared to a significant drop of 73% when using SMF at $-$ 24 dBm total coexistence power which is 250 times lower than the power used in HC-NANF. In the worst-case scenario using the same powers, and with 1 THz (8 nm) spacing between quantum and classical channels, the SKR dropped 10% using the HC-NANF, whereas in the SMF the SKR plummeted to zero. [ABSTRACT FROM AUTHOR]

In this paper, we propose a deep reinforcement learning (DRL)-based algorithm to generate policies of Baseband Function (BBF) placement and routing. In order to explore the performance of the proposed algorithm in practical systems, the online scenario with the completely random requests is used in the simulation considering C-RAN and NG-RAN architectures. Besides, an Integer Linear Programming (ILP) model is formulated to generate the optimal solution as the benchmark. The simulation results show that DRL-based algorithm converges in a short time, and its performance closes to the optimal benchmark obtained by ILP in terms of latency and bandwidth for the online scenarios. In addition, the performance of the generated policies based on DRL is compared with a classic heuristic algorithm, i.e., first-fit algorithm. The performance of DRL-based algorithm is superior to the first-fit algorithm from above two perspectives. The fast convergence and the near-optimal performance prove that the DRL-based algorithm is a promising approach for the BBF placement and routing of RAN in 5G and Beyond. [ABSTRACT FROM AUTHOR]

The study of Service Function Chains (SFCs) placement problem is crucial to support services flexibly and use resources efficiently. Solutions should satisfy various Quality of Service requirements, avoid edge resource congestion, and improve service acceptance ratio (SAR). This work presents a novel approach to address these challenges by solving a multi-objective SFCs placement problem based on the Pointer Network in multi-layer edge and cloud networks. We design a Deep Reinforcement Learning algorithm, called Chebyshev-assisted Actor-Critic SFCs Placement Algorithm, to overcome the limitations of traditional heuristic and evolutionary algorithms. Then, we run this algorithm iteratively with a set of weights to obtain non-dominated fronts, which have much higher hypervolume values than those obtained from other state-of-the-art algorithms. Moreover, running our algorithm individually with selected weights from non-dominated fronts can avoid edge resource congestion and achieve 98% SARs of low-latency services during high-workload periods. Finally, based on both simulation and real testbed experimental results, it is validated that the proposed algorithm fits for pragmatic service deployment while achieving 100% of SARs in the use cases deployed on the testbed. [ABSTRACT FROM AUTHOR]

To support the emerging 5G applications and the 5G bearer networks, optical networks, as the critical infrastructure, are continuously evolving to be more dynamic and automatic. The vision of future autonomous networks with low link margins requires precise estimation/prediction of the quality of transmission (QoT) of optical links. Machine learning (ML) technologies provide promising solutions to predict QoT of unestablished links. In this paper, we investigated hybrid modelling and transfer learning to address the key issues for deployment of ML applications in optical networks. The proposed approach for multiple-channel prediction reduces the training data requirement by 80% while obtaining the same MSE of 0.267dB compared with the model without transfer learning. The approach facilitates a streamlined ML life-cycle for data collection, training, and deployment. [ABSTRACT FROM AUTHOR]

This paper proposed and evaluated a programmable ROADM system for MCF-based SDM/WDM networks. The proposed ROADM system employing both bypass connection and Route-and-Select wavelength switching enables adaptable virtual topology in optical networks by dynamically configuring bypass connection cores. The simulation results confirmed this ROADM system could provide acceptable performance with an around 10–20% reduction in the total cost including the number of ports and WSSs by comparing with a fully flexible SDM/WDM ROADM system, which cannot be implemented due to the required extremely high-port-count WSSs. [ABSTRACT FROM AUTHOR]

Currently, telecommunication research communities are striving towards the adoption of Zero-touch network and Service Management (ZSM) in Network Function Virtualisation (NFV) orchestration. Contemporary efforts on adopting Machine Learning (ML) and Artificial Intelligence (AI) have caused an upsurge of ZSM application in the VNF space. While ML and AI complement the ZSM goals for building the intelligent NFV orchestration, a deep knowledge about the resource consumption by Network Services (NSs) and its constituent Virtual Network Functions (VNFs) is required, which would enable AI and ML models to manage the available resources better and enhance user experience. In this article, we propose a Novel Autonomous Profiling (NAP) method that not only predicts the optimum network load a VNF can support but also estimates the required resources in terms of CPU, Memory, and Network, to meet the performance targets and workload by utilising ML techniques. Our performance evaluation results on real datasets show that the output of NAP can be used in the next generation of NFV orchestration. [ABSTRACT FROM AUTHOR]

This paper proposes and demonstrates a hybrid-learning-assisted impairments abstraction framework for planning and provisioning intra- and inter-domain services in a field-trial multi-domain optical network testbed. The proposed abstraction strategy consists of a parametric and a non-parametric machine learning technique to allow the control plane to implement impairments abstraction with different accessible data or monitoring technologies in the data plane. The hybrid-learning-assisted abstraction framework aims to abstract the property of segmental links along the lightpath and combine them for end-to-end performance evaluation. By deploying the proposed abstraction framework, network providers or operators are able to exchange the abstracted information for end-to-end impairments abstraction without revealing detailed information within each network. We experimentally demonstrate the proposed solution over a three-network field-trial testbed with real monitored data. The hybrid-learning-assisted impairments abstraction proves to be an accurate abstraction tool, with an average of 0.33 dB end-to-end signal-to-noise-ratio estimation error for services across the three networks. [ABSTRACT FROM AUTHOR]

To address the limitations of current radio access networks (RANs), centralized RANs adopting the concept of flexible splits of the BBU functions between radio units (RUs) and the central unit have been proposed. This concept can be implemented combining both the Mobile Edge Computing model and relatively large-scale centralized Data Centers. This architecture requires high-bandwidth/low-latency optical transport networks interconnecting RUs and compute resources adopting SDN control. This paper proposes a novel mathematical model based on Evolutionary Game Theory that allows to dynamically identify the optimal split option with the objective to unilaterally minimize the infrastructure operational costs in terms of power consumption. Optimal placement of the SDN controllers is determined by a heuristic algorithm in such a way that guarantees the stability of the whole system. Finally, multi-agent learning methods were investigated in order to expand the model to more sophisticated scenarios where many RUs with limited information are interacting. [ABSTRACT FROM AUTHOR]

This paper focuses on a Centralized Radio Access Network solution adopting the concept of resource disaggregation. In this context, it proposes a heuristic suitable to optimally assign Base Band Unit processing functions in softwarized Radio Access Networks to different servers, taking into consideration their processing requirements with the aim to minimize the overall energy consumption. It also proposes the adoption of live migration of virtualized resources, in order to dynamically reallocate these functions to different servers that better match the continuously changing characteristics of 5G services, for increased energy efficiency purposes. The benefits associated with live migration are quantified through a series of experiments. Our results show a reduction of the number of switched-on servers through live migration that leads to a notable improvement in terms of resource and energy efficiency. [ABSTRACT FROM AUTHOR]

The feasibility of transmitting discrete-variable quantum key distribution channels with carrier-grade classical optical channels over multicore fibers is experimentally explored in terms of achievable quantum bit error rates, secret key rates as well as classical signal bit error rates. A coexistence transmission record of 11.2 Tb/s is achieved for the classical channels simultaneously with a DV-QKD channel over a 1 km-long 7-core multicore fiber. Coexistence over the same multicore fiber core is identified as a dominant factor for the performance of the quantum channel requiring optical bandpass filtering of 17 nm for the quantum channel to avoid the effect of Raman noise. Also, counter-propagation of classical channels and quantum channels probe more tolerance to noise proliferation than co-propagation. In addition, the performance of the quantum channel is maintained when more than three cores are used for the classical channels. Furthermore, by adding a second DV-QKD channel in the multicore fiber, the simultaneous transmission of classical channels as well as the generation of quantum-secured keys of two QKD channels is achieved with an operational range of 10 dB of launched power into the MCF. [ABSTRACT FROM AUTHOR]

In this article, artificial neural network (ANN)-based multi-channel Q-factor prediction is investigated with real-time network operation and configuration information over a 563.4-km field-trial testbed. A unified ANN-based regression model is proposed and implemented to predict Q-factors of all the channels simultaneously. A scenario generator is developed to configure the field-trial testbed with eight testing channels automatically to generate dynamic scenarios. A network configuration and monitoring database (CMDB) is implemented to collect network configuration and monitoring data that include link information, operational parameters of key optical devices, network configuration state, and real-time Q-factors of the available channels for the generated network scenarios. These collected data are used for training and testing of the developed ANN model. In order to achieve multiple channel predictions, we propose a hot coding method to represent the state of dynamic channel. Besides, an auto-search method is used to search the best hyperparameters of the ANN-based model. The results show that the proposed ANN-based regression model converges quickly, and it can predict the multi-channel's Q-factors with high accuracy. The unified ANN-based multi-channel Q-factor regression model can provide the comprehensive information to assist SDN controller to optimize network configuration for dynamic optical networks. [ABSTRACT FROM AUTHOR]

This article reports an FPGA-based P4-enabled Smart NIC solution which is designed and implemented for web-scale cloud and to meet 5G/beyond 5G networking requirements. The P4-enabled Smart NIC solution leverages the open standards, platforms and software-defined approaches, responds to the real time Data Centre Networking service requests, in particularly, enables the end-to-end network slicing, which is one of the critical requirements of a multi-tenancy 5G network. We discussed the possibilities and challenges of P4 specification implementation in the FPGA to realise the Smart NIC functionalities. And after that, we showed its data plane programmability and flexibility with P4 features. Furthermore, we demonstrated its application scenario in an 5G environment mainly focusing on edge Data Centre to core Data Centre network slicing. The setup interconnects the P4-enabled Smart NIC with optical Bandwidth Variable Transponders, and the system offers agile 100 Gbps interface to transport the packets through P4-defined data plane for L2/L3/L4 parsing and action. The P4-enabled Smart NIC can change the data plane pipelines in seconds, and it can achieve maximum 84.8 Gbps throughput. With P4 programmed hardware offloaded Segment Routing can produce 30% more bandwidth than without. [ABSTRACT FROM AUTHOR]